The autonomous driving has recently attracted great attention as a future technology. Basically, the autonomous driving technology includes recognizing surrounding objects and estimating distances between an autonomous vehicle and the surrounding objects, etc. Through the processes, the autonomous driving technology allows the autonomous vehicle to drive safely, preventing the autonomous vehicle from colliding with the surrounding objects.
The conventional autonomous driving technology includes a process of finding a vanishing point on an input image, which is obtained through a camera. Then, pitch calibration is performed through calculating an angle between a normal vector corresponding to lens surface of the camera and the ground by using relationship between the vanishing point and a principal point on the input image. Thereafter, by using the calculated angle, at least one distance between at least one object on the input image and the autonomous vehicle may be calculated.
Meanwhile, there are some problems in the conventional autonomous technology. For example, an accuracy of the distance calculation decreases sharply when a quality of the input image acquired through the camera is not good enough. Since the distance estimation is performed based on the input image, it may be natural that flaws in the images incur flaws in the distance estimation. However, the real problem is that, in most of cases, it is impossible for people to prevent the flaws in the images. Specifically, the problems such as diminishing lights in a tunnel or a blurred view incurred by rainy weather cannot be solved even if people use superior cameras or design the autonomous driving system well.
Therefore, there is a need for providing accurate calculations of the distances even when the quality of the input image acquired through the camera is not good enough.